The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, environment, and water resources. In this study, the Innovative Trend Pivot Analysis Method (ITPAM) and Trend Polygon Star Concept Method were applied for precipitation trend detection at eleven stations located in Soan River Basin (SRB), Potohar region Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting and calculating monthly shifts under different trend behaviors i.e. increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows complex nature of behaviour and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first data set and the second data set in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behaviour in the water resources and agricultural sectors.

The reliability of weather radar data in real-time flood forecasting and early warning system remain ambivalent due to high uncertainty in Quantitative Precipitation Forecasts (QPF). In this study, a methodology is presented with the objective to improve the flood forecasting results with the application of radar rainfall calculated in three different ways. The QPF radar rainfall forecast data of four typhoon events in Fèngshān River Basin, Taiwan, were simulated using the WASH123D numerical model. The simulated results were corrected using a physical real-time correction technique and compared with direct simulation without correction for all three QPF calculation methods. According to model performance evaluation criteria, in the third method of QPF calculation, flood peak error was the lowest in all three methods, indicating better results for flood forecasting and can be used for flood early warning systems. The impact of the real-time correction technique was assessed using mass balance analysis. It was found that flow change is between 16% and 42% from direct simulation, indicating being on the safe side in case of a flood warning. However, the impact of the real-time physical correction on the water level itself is in a reasonable range. Still, QPF rainfall correction/calculation is more important to obtain accurate results for flood forecasting. Therefore, the application of real-time correction to correct the model water level has a certain degree of credibility, which is the mass balance of the model. This approach is recommended for flood forecasting early warning systems.

Pakistan faces water scarcity and high operational costs for traditional irrigation systems, hindering agricultural productivity. Solar-powered irrigation systems (SPIS) can potentially provide a sustainable and affordable solution, but face technical, financial and policy barriers to adoption. A comprehensive study is needed to examine feasibility and identify barriers. Therefore, a comprehensive review study is conducted to identify the potential for solar irrigation, key issues and challenges related to its implementation in Pakistan. The analysis is based on published studies, technical reports and a survey of solar-powered drip irrigation systems. The use of SPIS in Pakistan is becoming a cost-effective and sustainable option for irrigation, particularly in remote and off-grid areas. However, these systems also have their challenges, such as high initial costs, maintenance and repairs, limited access to spare parts, lack of government policies and regulations, lack of technical expertise, lack of financing options and social acceptance. The most pressing issue is the risk of groundwater exploitation by using SPIS. Based on the analysis of the energy and water situation in Pakistan, it is important to sustainably use both solar energy and groundwater resources, through the implementation of effective management strategies and policies. With the right policies and investment in research and development of SPIS and groundwater, farmers can benefit by increasing crop yields, conserving water resources, reducing the cost of energy, increasing productivity and improving the standard of living and access to electricity in remote and off-grid areas. It is recommended that the adoption of solar energy be promoted to run high efficiency irrigation systems (HEIS) with urgent capacity improvement among farmers, advisors and system installers to sustainably manage water resources in SPIS. This would not only help to reduce the consumption of fossil fuels and associated environmental impacts, but also increase farmers’ income and reduce their operational costs. Moreover, the use of SPIS can improve crop yields, leading to food security and poverty reduction. Thus, the government and policymakers should consider implementing policies and incentives to encourage the large-scale adoption of solar energy in the agricultural sector.

The trend analysis approach is used to estimate changing climate and its impact on the environment, agriculture and water resources. Innovative polygonal trend analyses are qualitative methods applied to detect changes in the environment. In this study, the Innovative Trend Pivot Analysis Method (ITPAM) and Trend Polygon Star Concept Method were applied for temperature trend detection in Soan River Basin (SRB), Potohar region, Pakistan. The average monthly temperature data (1995–2020) for 11 stations were used to create polygon graphics. Trend length and slope were calculated separately for arithmetic mean and standard deviation. The innovative methods produced useful scientific information, with the identification of monthly shifts and trend behaviors of temperature data at different stations. Some stations showed an increasing trend and others showed decreasing behavior. This increasing and decreasing variability is the result of climate change. The winter season temperature is increasing, and the months of December to February are getting warmer. Summer is expanding and pushing autumn towards winter, swallowing the early period of the cold season. The monthly polygonal trends with risk graphs depicted a clear picture of climate change in the Potohar region of Pakistan. The phenomena of observed average temperature changes, indicated by both qualitative methods, are interesting and have the potential to aid water managers’ understanding of the cropping system of the Potohar region.